Cargando…

CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation

Laue microdiffraction is an X-ray diffraction technique that allows for the non-destructive acquisition of spatial maps of crystallographic orientation and the strain state of (poly)crystalline specimens. To do so, diffraction patterns, consisting of thousands of Laue spots, are collected and analyz...

Descripción completa

Detalles Bibliográficos
Autores principales: Kirstein, Tom, Petrich, Lukas, Purushottam Raj Purohit, Ravi Raj Purohit, Micha, Jean-Sébastien, Schmidt, Volker
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10180338/
https://www.ncbi.nlm.nih.gov/pubmed/37176279
http://dx.doi.org/10.3390/ma16093397
_version_ 1785041313036500992
author Kirstein, Tom
Petrich, Lukas
Purushottam Raj Purohit, Ravi Raj Purohit
Micha, Jean-Sébastien
Schmidt, Volker
author_facet Kirstein, Tom
Petrich, Lukas
Purushottam Raj Purohit, Ravi Raj Purohit
Micha, Jean-Sébastien
Schmidt, Volker
author_sort Kirstein, Tom
collection PubMed
description Laue microdiffraction is an X-ray diffraction technique that allows for the non-destructive acquisition of spatial maps of crystallographic orientation and the strain state of (poly)crystalline specimens. To do so, diffraction patterns, consisting of thousands of Laue spots, are collected and analyzed at each location of the spatial maps. Each spot of these so-called Laue patterns has to be accurately characterized with respect to its position, size and shape for subsequent analyses including indexing and strain analysis. In the present paper, several approaches for estimating these descriptors that have been proposed in the literature, such as methods based on image moments or function fitting, are reviewed. However, with the increasing size and quantity of Laue image data measured at synchrotron sources, some datasets become unfeasible in terms of computational requirements. Moreover, for irregular Laue spots resulting, e.g., from overlaps and extended crystal defects, the exact shape and, more importantly, the position are ill-defined. To tackle these shortcomings, a procedure using convolutional neural networks is presented, allowing for a significant acceleration of the characterization of Laue spots, while simultaneously estimating the quality of a Laue spot for further analyses. When tested on unseen Laue spots, this approach led to an acceleration of 77 times using a GPU while maintaining high levels of accuracy.
format Online
Article
Text
id pubmed-10180338
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-101803382023-05-13 CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation Kirstein, Tom Petrich, Lukas Purushottam Raj Purohit, Ravi Raj Purohit Micha, Jean-Sébastien Schmidt, Volker Materials (Basel) Article Laue microdiffraction is an X-ray diffraction technique that allows for the non-destructive acquisition of spatial maps of crystallographic orientation and the strain state of (poly)crystalline specimens. To do so, diffraction patterns, consisting of thousands of Laue spots, are collected and analyzed at each location of the spatial maps. Each spot of these so-called Laue patterns has to be accurately characterized with respect to its position, size and shape for subsequent analyses including indexing and strain analysis. In the present paper, several approaches for estimating these descriptors that have been proposed in the literature, such as methods based on image moments or function fitting, are reviewed. However, with the increasing size and quantity of Laue image data measured at synchrotron sources, some datasets become unfeasible in terms of computational requirements. Moreover, for irregular Laue spots resulting, e.g., from overlaps and extended crystal defects, the exact shape and, more importantly, the position are ill-defined. To tackle these shortcomings, a procedure using convolutional neural networks is presented, allowing for a significant acceleration of the characterization of Laue spots, while simultaneously estimating the quality of a Laue spot for further analyses. When tested on unseen Laue spots, this approach led to an acceleration of 77 times using a GPU while maintaining high levels of accuracy. MDPI 2023-04-26 /pmc/articles/PMC10180338/ /pubmed/37176279 http://dx.doi.org/10.3390/ma16093397 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kirstein, Tom
Petrich, Lukas
Purushottam Raj Purohit, Ravi Raj Purohit
Micha, Jean-Sébastien
Schmidt, Volker
CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation
title CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation
title_full CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation
title_fullStr CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation
title_full_unstemmed CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation
title_short CNN-Based Laue Spot Morphology Predictor for Reliable Crystallographic Descriptor Estimation
title_sort cnn-based laue spot morphology predictor for reliable crystallographic descriptor estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10180338/
https://www.ncbi.nlm.nih.gov/pubmed/37176279
http://dx.doi.org/10.3390/ma16093397
work_keys_str_mv AT kirsteintom cnnbasedlauespotmorphologypredictorforreliablecrystallographicdescriptorestimation
AT petrichlukas cnnbasedlauespotmorphologypredictorforreliablecrystallographicdescriptorestimation
AT purushottamrajpurohitravirajpurohit cnnbasedlauespotmorphologypredictorforreliablecrystallographicdescriptorestimation
AT michajeansebastien cnnbasedlauespotmorphologypredictorforreliablecrystallographicdescriptorestimation
AT schmidtvolker cnnbasedlauespotmorphologypredictorforreliablecrystallographicdescriptorestimation